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GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
14 years 10 months ago
Evolution of reward functions for reinforcement learning
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Scott Niekum, Lee Spector, Andrew G. Barto
CVPR
2009
IEEE
1848views Computer Vision» more  CVPR 2009»
17 years 13 days ago
Moving Cast Shadow Detection using Physics-based Features
Cast shadows induced by moving objects often cause serious problems to many vision applications. We present in this paper an online statistical learning approach to model the backg...
Jia-Bin Huang and Chu-Song Chen
ICML
2007
IEEE
16 years 7 months ago
On the role of tracking in stationary environments
It is often thought that learning algorithms that track the best solution, as opposed to converging to it, are important only on nonstationary problems. We present three results s...
Richard S. Sutton, Anna Koop, David Silver
CHI
2002
ACM
16 years 7 months ago
WebQuests: changing the way we teach online
This paper introduces WebQuests as potential teaching tools for HCI and software design educators. Based on our daylong observations of a high-school class, we believe that WebQue...
Brenda Hopkins-Moore, Susan Fowler
OTM
2005
Springer
16 years 2 days ago
OWL-Based User Preference and Behavior Routine Ontology for Ubiquitous System
In ubiquitous computing, behavior routine learning is the process of mining the context-aware data to find interesting rules on the user’s behavior, while preference learning tri...
Kim Anh Pham Ngoc, Young-Koo Lee, Sungyoung Lee